Evaluation of a new heart beat classification method based on ABC algorithm, comparison with GA, PSO and ACO classifiers Online publication date: Wed, 08-Apr-2015
by Selim Dilmac; Ali Nizam; Mehmet Korurek
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 6, No. 3/4, 2014
Abstract: In this paper, we proposed a new method modified artificial bee colony (MABC) algorithm and it is applied to ECG signal analysis for heart beat classification. MITBIH database ECG data is used. In this dataset, MABC algorithm can reach high classification success rate, even with the low values of colony size and other control parameters such as MCN and limit. The classification success rate result of MABC is compared with results of three other classifiers: GA, PSO and ACO. In classification problem, choosing distinctive features has important effect to get a high classification success rate. By using the right features on analysed dataset, high system classification success rate (98.73%) is achieved by MABC, similar to other compared classifiers. MABC and ACO has high sensitivity for all beat types while GA and PSO have lower classification success rates for some beat types.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Reasoning-based Intelligent Systems (IJRIS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook